Hillsborough, New Jersey, United States
Applied AI, Machine Learning Engineer and Ph.D. in Computer Science with 8+ years of experience spanning software engineering, data science, and AI-driven systems. Experienced in machine learning, Generative AI, Retrieval-Augmented Generation (RAG), GraphRAG, knowledge graphs, statistical modeling, and production-oriented ML pipelines. Strong background in graph analytics, network science, and translating research into scalable, production-ready AI applications.
Official Title: Research Project Assistant • Designed and implemented machine learning and Generative AI systems for sentiment analysis and opinion dynamics over large-scale networked data using Python, SQL, and PySpark. • Built end-to-end ML and LLM pipelines covering data ingestion, feature engineering, model development, evaluation, and iteration for research-to-production workflows. • Developed REST-style AI services using Flask and Streamlit to expose ML/LLM models for interactive use and integration with downstream applications. • Prototyped agentic LLM workflows for task automation, tool use, structured reasoning, and decision routing. • Applied probabilistic and regression-based modeling techniques to estimate latent behavioral and diffusion parameters in network systems. • Developed and applied Graph Neural Network (GNN) approaches for modeling information propagation. • Conducted statistical experimentation (hypothesis testing, uncertainty estimation) to validate model assumptions and performance stability. • Containerized ML and GenAI systems using Docker to enable reproducible research and deployment workflows.
• Instructed and guided undergraduate batches of nearly 30 students, in core courses - Java and Data Structures. • Designed and evaluated assignments, exams, and projects to assess students' understanding and progress. • Conducted lab sessions, providing hands-on programming support, and enhancing students' problem-solving skills.
• Performed data analysis and exploration using python to identify best model features. • Analyzed structured datasets (SQL) to generate internal reports used by engineering teams for decision-making.
• Queried and validated large-scale transactional datasets using DB2/SQL in financial systems. • Investigated production issues, performed data-level debugging, and supported system reliability.
• Led data migration, validation, and reconciliation for POS and payment systems using DB2/SQL. • Supported system integration and troubleshooting across multiple environments.